Understanding the etiology of developmental dyslexia (DD) is complicated by the frequent comorbidity of neurodevelopmental disorders such as specific language impairment (SLI) and ADHD, which increase sample heterogeneity. Project 2 addresses this heterogeneity by examining the neurocomputational mechanisms underiying treatment resistance and responses among children with DD and comorbid ADHD - a high-frequency comorbidity of 25-40%. To date, neurobiological studies have focused on abnormalities in brain and neurocognitive functionality for DD and ADHD separately, without particular focus on comorbid DD+ ADHD individuals. Clinical studies of DD+ADHD have identified unique variance associated with working memory and processing speed as common, underiying neurocognitive weakness. This Project will develop detailed behavioral and neurobiological profiles of DD + ADHD who are treatment resisters, and responders, to be analyzed with a focus on isolating specific factors that differentiate between them, and predict variability within these subgroups. It also seeks to advance the understanding of the neurobiology of DD with integrated cognitive and neuroimaging experiments designed to uncover those specific neurocomputational mechanisms associated with DD + ADHD which may be placing limits on processing speed, capacity, and learning and consolidation abilities. After referral and identification of both DD and ADHD, 80 children from Grades 3/4 will participate in Phase I, which includes 70 hours of empirically-validated reading intervention. DD+ADHD resisters and DD+ADHD-responders will be identified via psychometric and growth curve modeling techniques, with 20 of each response group identified for inclusion in Phase II studies. In Phase II, neuroimaging will be combined with dynamic learning and plasticity experiments utilizing speech and print as well as non-verbal materials. Projects 1 and 3 will provide comparison samples of grade 3/4 non-comorbid DD, TD, and comorbid DD+SLI, for comparing rates of response, predictors of response, and neurocognitive profiles based on imaging and behavioral learning experiments across groups. The integration of behavioral, neurobiological and computational data will provide a more complete picture of underiying impairments that give rise to similarities and differences between individuals and DD subgroups, particularly with regard to likelihood of response to intervention.